Seaborn Plotting 🤗

Seaborn is a Python data visualization library available on python, based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Functionalities of Seaborn

Data Visualization using Seaborn

Visualizing statistical relationships

Plotting categorical data

Visualizing the distribution of a dataset

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Importing Libraries

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Dist Plot

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Point Plot

A point plot represents an estimate of central tendency for a numeric variable by the position of scatter plot points and provides some indication of the uncertainty around that estimate using error bars.

Point plots can be more useful than bar plots for focusing comparisons between different levels of one or more categorical variables. They are particularly adept at showing interactions: how the relationship between levels of one categorical variable changes across levels of a second categorical variable. The lines that join each point from the same hue level allow interactions to be judged by differences in slope, which is easier for the eyes than comparing the heights of several groups of points or bars.

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Categorical Plots

Seaborn also helps us in doing the analysis on Categorical Data points.

Count Plot

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Bar Plot

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Box Plot

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Joint Plot

Seaborn’s jointplot displays a relationship between 2 variables (bivariate) as well as 1D profiles (univariate) in the margins. This plot is a convenience class that wraps JointGrid.

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Violin Plot

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Scatterplot

A scatterplot is perhaps the most common example of visualizing relationships between two variables. Each point shows an observation in the dataset and these observations are represented by dot-like structures. The plot shows the joint distribution of two variables using a cloud of points.

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Boxen Plot

A Boxen Plot is an an enhanced box plot for larger datasets.

This style of plot was originally named a "letter value" plot because it shows a large number of quantiles that are defined as "letter values". It is similar to a box plot in plotting a nonparametric representation of a distribution in which all features correspond to actual observations. By plotting more quantiles, it provides more information about the shape of the distribution, particularly in the tails.

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Strip Plot

A strip plot can be drawn on its own, but it is also a good complement to a box or violin plot in cases where you want to show all observations along with some representation of the underlying distribution.

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Swarm Plot

This function is similar to :func:stripplot, but the points are adjusted (only along the categorical axis) so that they don't overlap. This gives a better representation of the distribution of values, but it does not scale well to large numbers of observations. This style of plot is sometimes called a "beeswarm".

A swarm plot can be drawn on its own, but it is also a good complement to a box or violin plot in cases where you want to show all observations along with some representation of the underlying distribution.

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Pair Plot

A Pairs Plot is also know as scatterplot, in which one variable in the same data row is matched with another variable's value, like this: Pairs plots are just elaborations on this,showing all variables paired with all other variables

Conclusion:

From the dataset, we conclude that